Issue 5, 2021

One class classification as a practical approach for accelerating π–π co-crystal discovery


The implementation of machine learning models has brought major changes in the decision-making process for materials design. One matter of concern for the data-driven approaches is the lack of negative data from unsuccessful synthetic attempts, which might generate inherently imbalanced datasets. We propose the application of the one-class classification methodology as an effective tool for tackling these limitations on the materials design problems. This is a concept of learning based only on a well-defined class without counter examples. An extensive study on the different one-class classification algorithms is performed until the most appropriate workflow is identified for guiding the discovery of emerging materials belonging to a relatively small class, that being the weakly bound polyaromatic hydrocarbon co-crystals. The two-step approach presented in this study first trains the model using all the known molecular combinations that form this class of co-crystals extracted from the Cambridge Structural Database (1722 molecular combinations), followed by scoring possible yet unknown pairs from the ZINC15 database (21 736 possible molecular combinations). Focusing on the highest-ranking pairs predicted to have higher probability of forming co-crystals, materials discovery can be accelerated by reducing the vast molecular space and directing the synthetic efforts of chemists. Further on, using interpretability techniques a more detailed understanding of the molecular properties causing co-crystallization is sought after. The applicability of the current methodology is demonstrated with the discovery of two novel co-crystals, namely pyrene-6H-benzo[c]chromen-6-one (1) and pyrene-9,10-dicyanoanthracene (2).

Graphical abstract: One class classification as a practical approach for accelerating π–π co-crystal discovery

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Article information

Article type
Edge Article
04 Aug 2020
03 Dec 2020
First published
08 Dec 2020
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2021,12, 1702-1719

One class classification as a practical approach for accelerating π–π co-crystal discovery

A. Vriza, A. B. Canaj, R. Vismara, L. J. Kershaw Cook, T. D. Manning, M. W. Gaultois, P. A. Wood, V. Kurlin, N. Berry, M. S. Dyer and M. J. Rosseinsky, Chem. Sci., 2021, 12, 1702 DOI: 10.1039/D0SC04263C

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